With the application of video cameras in the field of urban security, image-information-based target tracking algorithms attract research enthusiasm of industry and academia. In the past three decades, the image tracking algorithm has made great progress. However, there are still many open problems that do not have perfect solutions, such as, large deformation of the target, change of viewing angles, change of lights, noisy background, interference, shelters, and other issues.
A traditional target tracking scheme is based on modeling of the target object completion algorithm, which mainly include three parts: 1) an extraction of the target feature model; 2) a multi-feature fusion model matching algorithm; and 3) a real-time update scheme of the algorithm.
Based on the traditional target tracking scheme, a tracker can deal with some traditional problems, such as large deformation of the target, change of lights and rapid movement. However, when a camouflage is existed in the environment, the image target cannot be well tracked since the image target is blocked. For example, in the crowd, when the tracking object (pedestrian) is blocked, the traditional image target tracking scheme may erroneously track other pedestrians.
Hence, how to improve the image target tracking scheme has become an important topic for the person skilled in the art.